Developing a deeper understanding of brain function requires researchers to connect the different levels of analysis that have characterized the field of neuroscience. In particular, the frontier of systems neuroscience is to reveal how the biophysical properties of neurons and the pattern and characteristics of their synaptic connections together give rise to a functional neural circuit. Measurements of neuronal biophysics, anatomical connectivity, synaptic currents, and circuit function are rarely performed on the same cells, and this experimental limitation has been a barrier to our integration of knowledge across these different levels. My proposal describes new techniques to classify cell types and measure anatomical connectivity, synaptic properties, and circuit output all in the same neurons. It also includes a new theoretical framework to integrate these measurements into a model to predict circuit function given its natural input. The neural circuits of the mouse retina provide an ideal model system for this integrated approach because of our extensive knowledge of cell types and the experimental accessibility of the retina, in which it can be stimulated with its natural input (patterns of light) while recording its full output (the spike trins of retinal ganglion cells). In addition to their impact as templates for the integration of measurements across levels to predict circuit function, the circuit maps of the mouse retina will provide critical insights into the segregation of visual processing between the retina and downstream visual areas in the brain. Detailed models linking synaptic connectivity to function will also aid in the diagnosis and treatment of retinal disease by associating particular circuit components with specific types of visual processing.